The present invention relates to mobile application selection and, more specifically, to a method that presents users of mobile devices with application ratings and reviews that most closely apply to their own set of circumstances.
With over 1.2 million iOS™ apps and 1.3 million Android® applications to choose from, finding the right application is a challenge for every mobile device user. Many users rely on application reviews to evaluate if an application is right for them. Application reviews are submitted by other mobile device users who have downloaded the application onto their own mobile device. The review typically consists of two parts: a star rating (typically 1 through 5 stars) and, optionally, a written description of the experience of the user with the application. The application reviews are made available to potential uses in an application store.
There are many criteria that define the suitability of an application for a given user. One user may rate an application poorly (a 1 star rating) if, for example, it performs slowly on their device. Another user, with a newer device, may not experience these problems and rate the application highly (5 stars). Similarly, a skiing application may receive contrasting ratings based on geography—a user in Colorado may love it (it does, after all, feature their favorite resort of Breckenridge) while a user in North Carolina may be disappointed (their local resort, Sugar Mountain, is nowhere to be found). Clearly there are many factors influencing application ratings, and application stores provide very little customization to cut out the noise and present a user with reviews and ratings of applications that apply to their specific situation. For example, the Google Play™ application store allows users to filter reviews based on application version (latest or all) and to order the reviews according to three criteria: newest, rating, and helpful. the Google Play™ application store also offers an additional filter for mobile devices—By Device—which shows only reviews that were received by those using the specific device of the user. These limited filtering and sorting capabilities leave users with potentially hundreds of reviews to scan through, many of which do not apply to their situation. Users in a different city, or running a different OS version, or on a different network carrier, or with differing storage capacities may all have different impressions of how an application meets their needs. Thus, there is a need in the art for a system and method of presenting potential users of mobile devices with applications ratings and reviews that most closely apply to their own specific set of circumstances.